Abstract
In this paper, we carry out an analytical and numerical comparative study of the main collision resolution approaches for LoRa network, namely: LoRa-ZigZag Decoding (L-ZD), LoRa-Capture Effect (L-CE), and LoRa-Signature Code (L-SC). We adopt a Markov-based approach for analytical modeling and performance evaluation. Furthermore, we consider through this study the main interesting performance metrics such as the throughput and the average packet delay. Our results show that the (L-CE) scheme outperforms the other schemes in terms of all performance metrics of interest. Besides, compared with the regular LoRa network, (L-CE) shows a significant throughput improvement of \(81,73\%\) followed by (L-ZD) with \(67,25\%\), and finally (L-SC) with an improvement of \(55,38\%\). Moreover, the access delay results show an impressive decrease of \(12,16\%\) for (L-CE), \(18,46\%\) for (L-SC), and \(15,32\%\) for (L-ZD).
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Amzil, A., Bellouch, A., Boujnoui, A., Hanini, M., Zaaloul, A. (2022). A Comparative Study of Three LoRa Collision Resolution Schemes: A Markov Model-Based Approach. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_11
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